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Semi-hydrogenation of alkynes to alkenes using electrochemical approaches is an appealing alternative to conventional thermocatalytic strategies, as it efficiently utilizes water as the hydrogen source at ambient temperature. However, the precise modulation of atomic and electronic structures of catalytic Pd active sites remains a persistent challenge, particularly in enhancing the conversion yield of alkynes and improving the selectivity of alkenes. Here, we synthesize Pd nanoparticles anchored onto the surface of the defective two-dimensional Fe2O3 support, referred to as Pd/Fe2O3 catalysts, to conduct the electrocatalytic semi-hydrogenation of alkynes. Intriguingly, we observed the reconstruction of the atomic structure and configuration of Pd nanoparticles in the Pd/Fe2O3 catalysts due to metal–support interaction, caused by the hybridization of Pd d and O p orbitals. This interaction significantly weakens the binding strength of Pd sites in the Pd/Fe2O3 catalysts to the chemisorbed 4-aminophenylacetylene and reactive hydrogen intermediates. Consequently, Pd/Fe2O3 catalysts achieve a high conversion rate (99%) and selectivity (99%) in the semi-hydrogenation of 4-aminophenylacetylene coupled with high faradaic efficiency, outperforming both benchmark commercial Pd/C and other reference catalysts. Additionally, terminal alkynes featuring diverse functionalized groups, including those with easily reducible or passivated functionalities, can be efficiently semi-hydrogenated using Pd/Fe2O3 catalysts. [ABSTRACT FROM AUTHOR]

In this paper, we investigate the well-posedness of stochastic variational inequalities on ℝ d with discontinuous drift coefficients. First, a case that drift coefficients are only integrable on (0 , T) × ℝ d is considered. Utilizing Girsanov's theorem, Zvonkin's transform and Yamada–Watanabe's principle, we derive the weak well-posedness if d > 1 and strong well-posedness if d = 1. Second, if the drift coefficients are piecewise Lipschitz continuous and d = 1 , strong well-posedness is also obtained by virtue of a transform which regularizes the discontinuous drifts. [ABSTRACT FROM AUTHOR]

Predicting the crystal structure is essential to address the reliance on serendipity for facilitating the discovery and design of high‐performance high‐entropy oxides (HEOs). Here, three classic algorithms‐based machine learning models to predict the crystal structure of HEOs are successfully established and analyzed by combining five metrics, and the XGBoost classifier shows excellent accuracy and robustness with ACC and F1 scores up to 0.977 and 0.975, respectively. SHAP summary plot indicates that the anion‐to‐cation radius ratio (rA/rC) has the greatest impact on crystal structure, followed by difference in Pauling and Mulliken electronegativities (ΔχPauling and ΔχMulliken). It is noteworthy that the rA/rC, ΔχPauling, and ΔχMulliken lower than 0.35, 0.1, and 0.2, respectively, tend to lead to a fluorite crystal structure, whereas rock‐salt and spinel crystal structures are always formed. This work is expected to facilitate the discovery and design of HEOs with tailorable crystal structures and properties. [ABSTRACT FROM AUTHOR]